# Semantic 3D Grid Maps for Autonomous Driving

Source: [Semantic 3D Grid Maps for Autonomous Driving](<https://www.sota2.com/research/paper/68fa6dd91e209131cea3c68c>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:56.093Z

Authors: Ajinkya Khoche, Maciej K Wozniak, Daniel Duberg, Patric Jensfelt

Published: 2022-11-03T10:52:39.000Z

## Abstract

Maps play a key role in rapidly developing area of autonomous driving. We survey the literature for different map representations and find that while the world is three-dimensional, it is common to rely on 2D map representations in order to meet real-time constraints. We believe that high levels of situation awareness require a 3D representation as well as the inclusion of semantic information. We demonstrate that our recently presented hierarchical 3D grid mapping framework UFOMap meets the real-time constraints. Furthermore, we show how it can be used to efficiently support more complex functions such as calculating the occluded parts of space and accumulating the output from a semantic segmentation network.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2211.01700>)

[PDF](<https://arxiv.org/pdf/2211.01700.pdf>)